Converging toward a common speech code: imitative and perceptuo-motor recalibration processes in speech production
Bibliographic record
Abstract
Auditory and somatosensory systems play a key role in speech motor control. In the act of speaking, segmental speech movements are programmed to reach phonemic sensory goals, which in turn are used to estimate actual sensory feedback in order to further control production. The adult's tendency to automatically imitate a number of acoustic-phonetic characteristics in another speaker's speech however suggests that speech production not only relies on the intended phonemic sensory goals and actual sensory feedback but also on the processing of external speech inputs. These online adaptive changes in speech production, or phonetic convergence effects, are thought to facilitate conversational exchange by contributing to setting a common perceptuo-motor ground between the speaker and the listener. In line with previous studies on phonetic convergence, we here demonstrate, in a non-interactive situation of communication, online unintentional and voluntary imitative changes in relevant acoustic features of acoustic vowel targets (fundamental and first formant frequencies) during speech production and imitation. In addition, perceptuo-motor recalibration processes, or after-effects, occurred not only after vowel production and imitation but also after auditory categorization of the acoustic vowel targets. Altogether, these findings demonstrate adaptive plasticity of phonemic sensory-motor goals and suggest that, apart from sensory-motor knowledge, speech production continuously draws on perceptual learning from the external speech environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".